Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

A Comparison of Seasonal Linear Models and Seasonal ARIMA Models for Forecasting Intra-Day Call Arrivals

  • Received : 20101000
  • Accepted : 20110100
  • Published : 2011.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In call forecasting literature, both the seasonal autoregressive integrated moving average(ARIMA) type models and seasonal linear models have been popularly suggested as competing models. However, their parallel comparison for the forecasting accuracy was not strictly investigated before. This study evaluates the accuracy of both the seasonal linear models and the seasonal ARIMA-type models when predicting intra-day call arrival rates using both real and simulated data. The seasonal linear models outperform the seasonal ARIMA-type models in both one-day-ahead and one-week-ahead call forecasting in our empirical study.

Keywords

References

  1. Bianchi, L., Jarrett, J. and Hanumara, R. C. (1993). Forecasting incoming calls to telemarketing centers, Journal of Business Forecasting Methods and Systems, 12, 3–12.
  2. Brown, L. D., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S. and Zhao, L. H. (2005). Statistical analysis of a telephone call center: A queueing-science perspective, Journal of the American Statistical Association, 100, 36–50.
  3. Curry, B. (2007). Neural networks and seasonality: Some technical considerations, European Journal of Operational Research, 179, 267–274. https://doi.org/10.1016/j.ejor.2006.03.012
  4. Gans, N., Koole, G. and Mandelbaum, A. (2003). Telephone call centers: Tutorial, review, and research prospects, Manufacturing and Service Operations Management, 5, 79–141.
  5. Shen, H. and Huang, J. Z. (2008). Interday forecasting and intra-day updating of call center arrivals, Manufacturing and Service Operations Management, 10, 391–410. https://doi.org/10.1287/msom.1070.0179
  6. Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing, Journal of the Operational Research Society, 54, 799–805. https://doi.org/10.1057/palgrave.jors.2601589
  7. Taylor, J.W. (2008). A comparison of univariate time series methods for forecasting intra-day arrivals at a call center, Management Science, 54, 253–265. https://doi.org/10.1287/mnsc.1070.0786
  8. Taylor, J. W. (2010). Exponentially weighted methods for forecasting intra-day time series with multiple seasonal cycles, International Journal of Forecasting, 26, 627–646.
  9. Tych, W., Pedregal, D. J., Young, P. C. and Davies, J. (2002). An unobserved component model for multi-rate forecasting of telephone call demand: The design of a forecasting support system, International Journal of Forecasting, 18, 673–695. https://doi.org/10.1016/S0169-2070(02)00071-7
  10. Weinberg, J., Brown, L. D. and Stroud, J. R. (2007). Bayesian forecasting of an inhomogeneous Poisson process with applications to call center data, Journal of the American Statistical Association, 102, 1185–1198.
  11. Zhang, G. P. and Qi, M. (2005). Neural network forecasting for seasonal and trend time series, European Journal of Operational Research, 160, 501–514. https://doi.org/10.1016/j.ejor.2003.08.037